How Computers Parse the Ambiguity of Everyday Language

If you’re one of the 2.4 million Twitter followers of the Hamilton impresario Lin-Manuel Miranda, you’ve come to expect a delightful stream of observations, including tweets capturing conversations with his son Sebastian, now 3 years old. Earlier this month, Miranda offered one such exchange under the title, “S’MORES. A Real-Life One-Act Play.”

Me: So that’s the marshmallow but you’re going to eat it with this graham cracker and chocolate.

[My son looks at me like I am the dumbest person alive.]

Sebastian: No, I’m going to eat it with my MOUTH.

[End of play.]

A charming slice of life, to be sure. But in that brief interaction, young Sebastian Miranda also inadvertently hit upon a kind of ambiguity that reveals a great deal about how people learn and process language—and how we might teach computers to do the same.

The misinterpretation on which the s’mores story hinges is hiding in the humble preposition with. Imagine the many ways one could finish this sentence:

I’m going to eat this marshmallow with …

If you’re in the mood for s’mores, then “graham cracker and chocolate” is an appropriate object of the preposition with. But if you want to split the marshmallow with a friend, you could say you’re going to eat it “with my buddy Charlie.” If you’re only grudgingly consuming that marshmallow, you could say you’re going eat it “with great reluctance.” Or you could say “with my hands” (or “with my mouth” like young Sebastian) if you’re focused on the method of eating.

Somehow speakers of English master these many possible uses of the word with without anyone specifically spelling it out for them. At least that’s the case for native speakers—in a class for English as a foreign language, the teacher likely would tease apart these nuances. But what if you wanted to provide the same linguistic education to a machine?

As it happens, just days after Miranda sent his tweet, computational linguists presented a conference paper exploring exactly why such ambiguous language is challenging for a computer-based system to figure out. The researchers did so using an online game that serves as a handy introduction to some intriguing work currently being done in the field of natural language processing (NLP).

Read more: The Atlantic

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